DOI: 10.1073/pnas.1903888116
论文题名: Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis
作者: Nisthal A. ; Wang C.Y. ; Ary M.L. ; Mayo S.L.
刊名: Proceedings of the National Academy of Sciences of the United States of America
ISSN: 0027-8424
出版年: 2019
卷: 116, 期: 33 起始页码: 16367
结束页码: 16377
语种: 英语
英文关键词: Mutagenesis
; Protein engineering
; Protein G
; Protein stability prediction
; Thermodynamic stability
Scopus关键词: amino acid
; protein
; algorithm
; amino acid sequence
; Article
; benchmarking
; chemical composition
; computer model
; controlled study
; correlational study
; high throughput sequencing
; hydrophobicity
; mutagenesis
; mutational analysis
; predictive value
; priority journal
; protein analysis
; protein engineering
; protein stability
; sensitivity analysis
; thermodynamics
; thermostability
; amino acid substitution
; chemistry
; computer simulation
; genetics
; mutagenesis
; mutation
; protein domain
; Amino Acid Substitution
; Amino Acids
; Computer Simulation
; Mutagenesis
; Mutation
; Protein Domains
; Protein Engineering
; Protein Stability
; Proteins
; Thermodynamics
英文摘要: The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability indirectly. Here, we develop an automated method to generate thermodynamic stability data for nearly every single mutant in a small 56-residue protein. Analysis reveals that most single mutants have a neutral effect on stability, mutational sensitivity is largely governed by residue burial, and unexpectedly, hydrophobics are the best tolerated amino acid type. Correlating the output of various stability-prediction algorithms against our data shows that nearly all perform better on boundary and surface positions than for those in the core and are better at predicting large-to-small mutations than small-to-large ones. We show that the most stable variants in the single-mutant landscape are better identified using combinations of 2 prediction algorithms and including more algorithms can provide diminishing returns. In most cases, poor in silico predictions were tied to compositional differences between the data being analyzed and the datasets used to train the algorithm. Finally, we find that strategies to extract stabilities from high-throughput fitness data such as deep mutational scanning are promising and that data produced by these methods may be applicable toward training future stability-prediction tools. © 2019 National Academy of Sciences. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163541
Appears in Collections: 气候变化与战略
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作者单位: Nisthal, A., Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States, Protein Engineering, Xencor, Inc., Monrovia, CA 91016, United States; Wang, C.Y., Protabit, LLC, Pasadena, CA 91106, United States; Ary, M.L., Protabit, LLC, Pasadena, CA 91106, United States; Mayo, S.L., Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, United States, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, United States
Recommended Citation:
Nisthal A.,Wang C.Y.,Ary M.L.,et al. Protein stability engineering insights revealed by domain-wide comprehensive mutagenesis[J]. Proceedings of the National Academy of Sciences of the United States of America,2019-01-01,116(33)